The Effect Of Foreign Aid on Corruption in Varying Regime Types

Sahar Sajid

Table of Contents

  1. Background
    1. Context
    2. Research Question
  2. Literature
  3. Empirics
    1. Argument
    2. Data
      1. figure A
      2. figure B
      3. figure C
      4. figure D
    3. Findings
  4. Limitations and Critiques
  5. Conclusion

Background

Background

Context

  • Focus on Global South: Latin America, Sub-Saharan Africa, etc.

  • Low income Countries: More affected by aid, variance in impacts depending on income levels

    source: World Bank

Background

Research Questions

  • Foreign aid continues to be a primary tool of global engagement

  • Few studies fully integrate regime type, donor leverage, and transparency outcomes all together.

  • Can lead to more of an understanding in terms of how to fulfill donor goals in various regime types

Background

Research Questions

Today:

Research Question

How does foreign aid affect corruption in varying regime types?

Literature

Literature

  • Camp One: more corruption in autocracies, minimal to no impact on democracies

    Kono et al., 2009

    Nieto-Matiz et. al. ,2020

  • Camp Two: Aid always causes corruption: regime type has no impact

    Knack, 2003

    Kaylvitis et al., 2012

    Bader et al., 201

  • Camp Three: Donor intent matters not regime type

    Bermeo, 2011

    Brown, 2005

Empirics

Empirics

Argument and Theory

  • Autocracies: Lack of voter base leads to uncontrolled growth in corruption

  • Democracies: Corruption increases, need to appease voter base decreases it

Empirics

Data

Figure A:

Sources: Transparency International: CPI, World Bank: Income Level and Percent GNI of ODA, V-Dem: Regime Classification

Approach: Differences in Differences:

\[ \text{CPI}_{it} = \sum_{k = -4}^{10} \beta_k \cdot \mathbb{1}(\text{EventTime}_{it} = k) + \alpha_i + \gamma_t + \varepsilon_{it} \]

  • \(\text{CPI}_{it}\): is the corruption score for country \(i\) in year \(t\),

  • \(\mathbb{1}(\text{EventTime}_{it} = k)\): indicator for each year relative to treatment. -\(\alpha_i\): Country fixed effects -\(\gamma_t\)-:year fixed effects are included to account for time-invariant country characteristics and common shocks.

Empirics

Data

Figure B:

sources:

Empirics

Data

Figure C:

sources:

Empirics

Data

Figure D: add figure D when you figure out how to fix the N/A thing

Empirics

Findings

  • Figure A: two contrasting trends:

    – Democracies: corruption decreases after an initial spike,

    – Autocracies: corruption steadily worsens following the aid shock

  • Figure B: democracies have more of a lag: shows institutional checks, shows why corruption improvement in figure A is not immediate in democracies

  • Figure C:

    – Autocracies: Aid goes in short term sectors,

    – Democracies: Aid goes to government

  • Figure D: Protests in both

    – Democracies: decrease in corruption

    – Autocracies: corruption continues to increase

Limitations and Critiques

Limitations and Critqiues

  • Difference-in-Differences event study assumes that treated and control countries would have followed similar paths in the absence of high ODA inflows

  • Excludes sub-national differences:

    – Somaliland

  • CPI is a perception-based measure, which may lag behind actual corruption changes or be biased by media coverage and international scrutiny.

  • Ignores Conditionality

  • Future studies could look at:

    – Sub-national variation

    – Diffrent economic levels

    – Donor type: Members of OPEC versus external sources i.e Chinao